Sleep Health and Lifestyle Dataset Analysis Report¶

Introduction: The Sleep Health and Lifestyle Dataset provides valuable insights into various factors affecting sleep patterns and overall lifestyle. With 400 rows and 13 columns, the dataset covers a wide range of variables including sleep duration, quality of sleep, physical activity levels, stress levels, BMI categories, cardiovascular health metrics, and the presence of sleep disorders. This report aims to analyze and interpret key findings from the dataset through descriptive statistics and data visualization techniques.

Dataset Overview:

Total Rows: 400 Total Columns: 13 Data Types: Float64, Int64, Object Missing Values: Present in 'Sleep Disorder' column (155 null values)

Key Features of the Dataset:

Comprehensive Sleep Metrics: The dataset includes variables such as sleep duration and quality, allowing for the exploration of sleep patterns and factors influencing sleep.

Lifestyle Factors: Physical activity levels and stress levels are included, providing insights into the impact of lifestyle on sleep health.

Cardiovascular Health: Blood pressure and heart rate measurements offer an understanding of participants' cardiovascular health.

Sleep Disorder Analysis: The presence or absence of sleep disorders such as Insomnia and Sleep Apnea enables the identification of individuals with specific sleep-related issues.

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
In [2]:
df = pd.read_csv("Sleep_health_and_lifestyle_dataset.csv")
In [3]:
df.head()
Out[3]:
Person ID Gender Age Occupation Sleep Duration Quality of Sleep Physical Activity Level Stress Level BMI Category Blood Pressure Heart Rate Daily Steps Sleep Disorder
0 1 Male 27 Software Engineer 6.1 6 42 6 Overweight 126/83 77 4200 NaN
1 2 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 NaN
2 3 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 NaN
3 4 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
4 5 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 374 entries, 0 to 373
Data columns (total 13 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   Person ID                374 non-null    int64  
 1   Gender                   374 non-null    object 
 2   Age                      374 non-null    int64  
 3   Occupation               374 non-null    object 
 4   Sleep Duration           374 non-null    float64
 5   Quality of Sleep         374 non-null    int64  
 6   Physical Activity Level  374 non-null    int64  
 7   Stress Level             374 non-null    int64  
 8   BMI Category             374 non-null    object 
 9   Blood Pressure           374 non-null    object 
 10  Heart Rate               374 non-null    int64  
 11  Daily Steps              374 non-null    int64  
 12  Sleep Disorder           155 non-null    object 
dtypes: float64(1), int64(7), object(5)
memory usage: 38.1+ KB
In [5]:
df.drop("Person ID",axis=1,inplace=True)
In [6]:
df.isnull().sum()
Out[6]:
Gender                       0
Age                          0
Occupation                   0
Sleep Duration               0
Quality of Sleep             0
Physical Activity Level      0
Stress Level                 0
BMI Category                 0
Blood Pressure               0
Heart Rate                   0
Daily Steps                  0
Sleep Disorder             219
dtype: int64
In [7]:
print(df['Sleep Disorder'].value_counts())

Sleep Disorder
Sleep Apnea    78
Insomnia       77
Name: count, dtype: int64

variables selected :¶

  1. Gender
  2. quality of sleep
  3. BMI category
In [8]:
## bar plot for BMI category

plt.figure(figsize=(8, 6))
bmi_category_counts = df['BMI Category'].value_counts()
bmi_category_counts.plot(kind='bar', color=['lightcoral', 'gold', 'lightblue'])
plt.title('BMI Category Distribution')
plt.xlabel('BMI Category')
plt.ylabel('Count')
plt.show()
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In [9]:
plt.figure(figsize=(8, 6))
gender_counts = df['Gender'].value_counts()
gender_counts.plot(kind='bar', color=['lightcoral', 'blue'])
plt.title('Gender Distribution')
plt.xlabel('Gender')
plt.ylabel('Count')
plt.show()
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In [10]:
# pie chart for Gender
gender_count = df["Gender"].value_counts().reset_index()
fig = px.pie(gender_count,values='count',names="Gender")
fig.show()
In [11]:
# pie chart for Gender
bmi_count = df["BMI Category"].value_counts().reset_index()
fig = px.pie(bmi_count,values='count',names="BMI Category")
fig.show()
In [12]:
from pandas.plotting import scatter_matrix
features = df.columns
plt.figure(figsize=(12, 12))
scatter_matrix(df[features], figsize=(15, 15), diagonal='kde')
plt.show()

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In [13]:
df = pd.concat([df, df['Blood Pressure'].str.split('/', expand=True)], axis=1).drop('Blood Pressure', axis=1)
df = df.rename(columns={0: 'BloodPressure_Upper', 1: 'BloodPressure_Lower'})
df['BloodPressure_Upper'] = df['BloodPressure_Upper'].astype(float)
df['BloodPressure_Lower'] = df['BloodPressure_Lower'].astype(float)
In [14]:
from sklearn import preprocessing 
label_encoder = preprocessing.LabelEncoder()
df['Gender'] = label_encoder.fit_transform(df['Gender'])
df['Occupation'] = label_encoder.fit_transform(df['Occupation'])
df['BMI Category'] = label_encoder.fit_transform(df['BMI Category'])
df['Sleep Disorder'] = label_encoder.fit_transform(df['Sleep Disorder'])
df.head()
Out[14]:
Gender Age Occupation Sleep Duration Quality of Sleep Physical Activity Level Stress Level BMI Category Heart Rate Daily Steps Sleep Disorder BloodPressure_Upper BloodPressure_Lower
0 1 27 9 6.1 6 42 6 3 77 4200 2 126.0 83.0
1 1 28 1 6.2 6 60 8 0 75 10000 2 125.0 80.0
2 1 28 1 6.2 6 60 8 0 75 10000 2 125.0 80.0
3 1 28 6 5.9 4 30 8 2 85 3000 1 140.0 90.0
4 1 28 6 5.9 4 30 8 2 85 3000 1 140.0 90.0
In [15]:
df.corr()
Out[15]:
Gender Age Occupation Sleep Duration Quality of Sleep Physical Activity Level Stress Level BMI Category Heart Rate Daily Steps Sleep Disorder BloodPressure_Upper BloodPressure_Lower
Gender 1.000000 -0.596358 -0.219113 -0.121579 -0.291366 0.001454 0.396018 -0.352060 0.217105 -0.014509 0.161131 -0.210527 -0.268378
Age -0.596358 1.000000 0.231188 0.344709 0.473734 0.178993 -0.422344 0.511329 -0.225606 0.057973 -0.303962 0.605878 0.593839
Occupation -0.219113 0.231188 1.000000 -0.325775 -0.278071 -0.103660 0.021123 0.699504 0.043928 -0.105877 -0.600787 0.518561 0.521835
Sleep Duration -0.121579 0.344709 -0.325775 1.000000 0.883213 0.212360 -0.811023 -0.376358 -0.516455 -0.039533 0.382045 -0.180406 -0.166570
Quality of Sleep -0.291366 0.473734 -0.278071 0.883213 1.000000 0.192896 -0.898752 -0.312562 -0.659865 0.016791 0.357477 -0.121632 -0.110151
Physical Activity Level 0.001454 0.178993 -0.103660 0.212360 0.192896 1.000000 -0.034134 0.077156 0.136971 0.772723 0.109225 0.265416 0.382651
Stress Level 0.396018 -0.422344 0.021123 -0.811023 -0.898752 -0.034134 1.000000 0.163895 0.670026 0.186829 -0.181296 0.102818 0.091811
BMI Category -0.352060 0.511329 0.699504 -0.376358 -0.312562 0.077156 0.163895 1.000000 0.295585 -0.005059 -0.725786 0.714057 0.750869
Heart Rate 0.217105 -0.225606 0.043928 -0.516455 -0.659865 0.136971 0.670026 0.295585 1.000000 -0.030309 -0.220913 0.294143 0.271092
Daily Steps -0.014509 0.057973 -0.105877 -0.039533 0.016791 0.772723 0.186829 -0.005059 -0.030309 1.000000 0.161275 0.103342 0.241986
Sleep Disorder 0.161131 -0.303962 -0.600787 0.382045 0.357477 0.109225 -0.181296 -0.725786 -0.220913 0.161275 1.000000 -0.539280 -0.523490
BloodPressure_Upper -0.210527 0.605878 0.518561 -0.180406 -0.121632 0.265416 0.102818 0.714057 0.294143 0.103342 -0.539280 1.000000 0.972885
BloodPressure_Lower -0.268378 0.593839 0.521835 -0.166570 -0.110151 0.382651 0.091811 0.750869 0.271092 0.241986 -0.523490 0.972885 1.000000
In [16]:
import seaborn as sns
def corr_vis(corr) :
    mask = np.zeros_like(corr)
    mask[np.triu_indices_from(mask)] = True
    with sns.axes_style("white"):
        f, ax = plt.subplots(figsize=(10, 7))
        g = sns.heatmap(corr, mask=mask, vmax=.3, square=True, annot=True, cmap='coolwarm')
        g.set_xticklabels(g.get_xticklabels(), rotation = 90, fontsize = 10)

num_corr = df.corr()
corr_vis(df.corr())

C:\Users\manip\anaconda3\Lib\site-packages\seaborn\matrix.py:260: FutureWarning:

Format strings passed to MaskedConstant are ignored, but in future may error or produce different behavior
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In [17]:
# Box plot: Gender vs Quality of Sleep
plt.figure(figsize=(8, 6))
df.boxplot(column='Quality of Sleep', by='Gender', grid=False, patch_artist=True)
plt.title('Gender vs Quality of Sleep')
plt.suptitle('')  # Removing the default super title
plt.xlabel('Gender')
plt.ylabel('Quality of Sleep')
plt.show()

# Box plot: BMI Category vs Quality of Sleep
plt.figure(figsize=(8, 6))
df.boxplot(column='Quality of Sleep', by='BMI Category', grid=False, patch_artist=True)
plt.title('BMI Category vs Quality of Sleep')
plt.suptitle('')
plt.xlabel('BMI Category')
plt.ylabel('Quality of Sleep')
plt.show()

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In [18]:
import seaborn as sns
plt.figure(figsize=(8, 6))
sns.histplot(df['Quality of Sleep'], bins=10, kde=True, color='lightgreen')
plt.title('Histogram of Quality of Sleep')
plt.xlabel('Quality of Sleep')
plt.ylabel('Frequency')

# Show the plot
plt.show()

C:\Users\manip\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
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In [19]:
plt.figure(figsize=(8, 6))
sns.histplot(df['Sleep Duration'], bins=10, kde=True, color='salmon')
plt.title('Histogram of Sleep Duration')
plt.xlabel('Sleep Duration')
plt.ylabel('Frequency')

# Show the plot
plt.show()

C:\Users\manip\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
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In [20]:
# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score
In [21]:
df.dropna()
Out[21]:
Gender Age Occupation Sleep Duration Quality of Sleep Physical Activity Level Stress Level BMI Category Heart Rate Daily Steps Sleep Disorder BloodPressure_Upper BloodPressure_Lower
0 1 27 9 6.1 6 42 6 3 77 4200 2 126.0 83.0
1 1 28 1 6.2 6 60 8 0 75 10000 2 125.0 80.0
2 1 28 1 6.2 6 60 8 0 75 10000 2 125.0 80.0
3 1 28 6 5.9 4 30 8 2 85 3000 1 140.0 90.0
4 1 28 6 5.9 4 30 8 2 85 3000 1 140.0 90.0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
369 0 59 5 8.1 9 75 3 3 68 7000 1 140.0 95.0
370 0 59 5 8.0 9 75 3 3 68 7000 1 140.0 95.0
371 0 59 5 8.1 9 75 3 3 68 7000 1 140.0 95.0
372 0 59 5 8.1 9 75 3 3 68 7000 1 140.0 95.0
373 0 59 5 8.1 9 75 3 3 68 7000 1 140.0 95.0

374 rows × 13 columns

In [22]:
df.isnull().sum()
Out[22]:
Gender                     0
Age                        0
Occupation                 0
Sleep Duration             0
Quality of Sleep           0
Physical Activity Level    0
Stress Level               0
BMI Category               0
Heart Rate                 0
Daily Steps                0
Sleep Disorder             0
BloodPressure_Upper        0
BloodPressure_Lower        0
dtype: int64
In [23]:
## variables to be selected :
# Step 1: Prepare the data (Features and Target)
# In this case, we predict Sleep Disorder (convert to binary: 0 - None, 1 - has disorder)
df['Sleep Disorder'] = df['Sleep Disorder'].apply(lambda x: 0 if x == 'Sleep Apnea' else 1)
In [24]:
# Select features and target
# We are adding 'Heart Rate' to the feature set now
X = df[['Sleep Duration', 'Stress Level', 'Physical Activity Level', 'Heart Rate']]  # Feature columns
y = df['Sleep Disorder']   # Target column (labels)
In [25]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)
In [26]:
logistic_model = LogisticRegression()
logistic_model.fit(X_train, y_train)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[26], line 2
      1 logistic_model = LogisticRegression()
----> 2 logistic_model.fit(X_train, y_train)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1151, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1144     estimator._validate_params()
   1146 with config_context(
   1147     skip_parameter_validation=(
   1148         prefer_skip_nested_validation or global_skip_validation
   1149     )
   1150 ):
-> 1151     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1252, in LogisticRegression.fit(self, X, y, sample_weight)
   1250 classes_ = self.classes_
   1251 if n_classes < 2:
-> 1252     raise ValueError(
   1253         "This solver needs samples of at least 2 classes"
   1254         " in the data, but the data contains only one"
   1255         " class: %r"
   1256         % classes_[0]
   1257     )
   1259 if len(self.classes_) == 2:
   1260     n_classes = 1

ValueError: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1
In [ ]: